Determinants of total factor productivity in Visegrad Group Nuts-2 regions

2018 ◽  
Vol 68 (1) ◽  
pp. 31-50 ◽  
Author(s):  
Barbara Danska-Borsiak
Keyword(s):  
Panel Data ◽  
Total Factor Productivity ◽  
Latent Variables ◽  
Fixed Effects ◽  
Orthogonal Transformation ◽  
Common Factors ◽  
Dynamic Panel Data ◽  
Factor Productivity ◽  
The Common ◽  
Visegrad Group

This article attempts to estimate the total factor productivity (TFP) for 35 NUTS-2 regions of the Visegrad Group countries and to identify its determinants. The TFP values are estimated on the basis of the Cobb-Douglas production function, with the assumption of regional differences in productivity. The parameters of the productivity function were analysed with panel data, using a fixed effects model. There are many economic variables that influence the TFP level. Some of them are highly correlated, and therefore the factor analysis was applied to extract the common factors – the latent variables that capture the common variance among those observed variables that have similar patterns of responses. This statistical procedure uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Each component is interpreted using the contributions of variables to the respective component. I estimated a dynamic panel data model describing TFP formation by regions. An attempt was made to incorporate the common factors among the model’s explanatory variables. One of them, representing the effects of research activity, proved to be significant.

Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects

2019 ◽  
Vol 7 (4) ◽  
pp. 330-343
Author(s):  
Bianling Ou ◽  
Zhihe Long ◽  
Wenqian Li
Keyword(s):  
Panel Data ◽  
Spatial Dependence ◽  
Fixed Effects ◽  
Orthogonal Transformation ◽  
Transformation Method ◽  
Data Models ◽  
Panel Data Models ◽  
Significance Level ◽  
Lm Tests ◽  
Nominal Significance

Abstract This paper applies bootstrap methods to LM tests (including LM-lag test and LM-error test) for spatial dependence in panel data models with fixed effects, and removes fixed effects based on orthogonal transformation method proposed by Lee and Yu (2010). The consistencies of LM tests and their bootstrap versions are proved, and then some asymptotic refinements of bootstrap LM tests are obtained. It shows that the convergence rate of bootstrap LM tests is O((NT)−2) and that of fast double bootstrap LM tests is O((NT)−5/2). Extensive Monte Carlo experiments suggest that, compared to aysmptotic LM tests, the size of bootstrap LM tests gets closer to the nominal level of signifiance, and the power of bootstrap LM tests is higher, especially in the cases with small spatial correlation. Moreover, when the error is not normal or with heteroskedastic, asymptotic LM tests suffer from severe size distortion, but the size of bootstrap LM tests is close to the nominal significance level. Bootstrap LM tests are superior to aysmptotic LM tests in terms of size and power.

scholarly journals The Relationship between Environmental Regulation and Green Total Factor Productivity in China: An Empirical Study Based on the Panel Data of 177 Cities

2020 ◽  
Vol 17 (15) ◽  
pp. 5287 ◽  
Author(s):  
Mingliang Zhao ◽  
Fangyi Liu ◽  
Wei Sun ◽  
Xin Tao
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Inflection Point ◽  
Factor Productivity ◽  
Positive Effects ◽  
Development Level ◽  
The Mean ◽  
The Relationship

Promoting the coordinated development of industrialization and the environment is a goal pursued by all of the countries of the world. Strengthening environmental regulation (ER) and improving green total factor productivity (GTFP) are important means to achieving this goal. However, the relationship between ER and GTFP has been debated in the academic circles, which reflects the complexity of this issue. This paper empirically tested the relationship between ER and GTFP in China by using panel data and a systematic Gaussian Mixed Model (GMM) of 177 cities at the prefecture level. The research shows that the relationship between ER and GTFP is complex, which is reflected in the differences and nonlinearity between cities with different monitoring levels and different economic development levels. (1) The relationship between ER and GTFP is linear and non-linear in different urban groups. A positive linear relationship was found in the urban group with high economic development level, while a U-shaped nonlinear relationship was found in other urban groups. (2) There are differences in the inflection point value and the variable mean of ER in different urban groups, which have different promoting effects on GTFP. In key monitoring cities and low economic development level cities, the mean value of ER had not passed the inflection point, and ER was negatively correlated with GTFP. The mean values of ER variables in the whole sample, the non-key monitoring and the middle economic development level cities had all passed the inflection point, which gradually promoted the improvement of GTFP. (3) Among the control variables of the different city groups, science and technology input and the financial development level mainly had positive effects on GTFP, while foreign direct investment (FDI) and fixed asset investment variables mainly had negative effects.

scholarly journals Exports, Misallocation, and Total Factor Productivity of Furniture Enterprises

2019 ◽  
Vol 11 (18) ◽  
pp. 4892
Author(s):  
Chang Xu ◽  
Jianbing Guo ◽  
Baodong Cheng ◽  
Yu Liu
Keyword(s):  
Total Factor Productivity ◽  
Value Chain ◽  
Fixed Effects ◽  
Negative Impact ◽  
Market Competition ◽  
Fixed Effects Model ◽  
Factor Productivity ◽  
Labor Costs ◽  
Negative Effect ◽  
The Impact

With the increase in labor costs in China and the tremendous changes in the international trade environment, upgrading the total factor productivity of Chinese furniture export enterprises faces a great challenge. Lots of studies have explored the interaction of exports or misallocation on the total factor productivity (TFP) of furniture enterprises, however, there is little knowledge on the impact and interaction of both exports and misallocation on the TFP. Based on panel data of Chinese furniture enterprises, this paper measures the TFP and the distortion of labor and capital resources in Chinese furniture enterprises. A two-way fixed-effects model is used to analyze the impact of exports and misallocation on the TFP of Chinese furniture enterprises. The paper reveals several important findings. First, the TFP of Chinese furniture export enterprises is lower than that of non-export enterprises, this phenomenon is called the “export–productivity paradox”. Chinese furniture export enterprises are processing trade-oriented and labor-intensive enterprises at the low end of the value chain, exports have a negative effect on improving the TFP of furniture enterprises in the short term. Second, the distortion of labor and capital resources in Chinese furniture enterprises promotes improvements to the TFP of furniture enterprises rather than reducing the TFP of furniture enterprises. Last but not the least, we find that misallocation has a positive moderating effect on exports and can weaken the negative impact of exports on TFP by the “forced mechanism”, which is that the higher the distortion of the misallocation, the higher the cost of acquiring capital and labor, and enterprises are forced to enhance their productivity when facing market competition, thus promoting improvements to the TFP of furniture enterprises.

scholarly journals Bias correction methods for dynamic panel data models with fixed effects

2017 ◽  
Vol 6 (2) ◽  
pp. 58
Author(s):  
Mohamed Abonazel
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Estimation Methods ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Cross Sectional Dimension ◽  
Ls Estimator

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

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